{"id":"W2472302001","doi":"10.1007/978-3-319-27702-8_9","title":"Online Loop-Closure Detection via Dynamic Sparse Representation","year":2016,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sparse approximation; Simultaneous localization and mapping; Closure (psychology); Representation (politics); Loop (graph theory); Matching (statistics); Artificial intelligence; Scalability; Minification; Algorithm; Computer vision; Robot; Mathematics; Mobile robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057346,0.001104693,0.00153526,0.0007130397,0.0003387523,0.001122996,0.00116536,0.001272326,0.002946461],"category_scores_gemma":[0.004155062,0.0007024891,0.0005977172,0.0007984971,0.0006536368,0.001940451,0.002188485,0.001812013,0.001420463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000272536,"about_ca_system_score_gemma":0.000625027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001116156,"about_ca_topic_score_gemma":0.001578391,"domain_scores_codex":[0.9992715,0.0001131015,0.00003651641,0.0002252681,0.0002872219,0.00006643149],"domain_scores_gemma":[0.9985104,0.0008576784,0.0001450119,0.0002223801,0.0002166946,0.00004783563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003058622,0.000142094,0.0004193632,0.0002310594,0.00006896985,0.000169002,0.0001125132,0.1321455,0.0376369,0.01391852,0.009107203,0.8057429],"study_design_scores_gemma":[0.00001295076,0.00005216753,0.0001382726,0.00001412377,0.000008796599,0.0001197981,0.00001366504,0.9832621,0.005812216,0.008958098,0.001595151,0.00001274969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002701253,0.0001698106,0.9957788,0.00007618549,0.00005468716,0.00001493099,0.0000355261,0.0004525852,0.0007162218],"genre_scores_gemma":[0.2590576,0.0006649994,0.7306896,0.0002817136,0.0002742254,0.0001570052,0.0006659396,0.0003219859,0.007886864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002946461,"threshold_uncertainty_score":0.009856939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359356395772906,"score_gpt":0.2378018821491533,"score_spread":0.2242083181914242,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}